We address the problem of supporting radiologists in the longitudinal management of lung cancer. Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN network the estimated nodule malignancy probabilities derived from a pretrained nodule malignancy network. The pipeline was evaluated in a longitudinal cohort and reported comparable performances to the state of art.
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Thoracic Department, Fujian Medical University Union Hospital（福建医科大学附属协和医院胸外科）
Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint…
Candidate generation, the first stage for most computer aided detection (CAD) systems, rapidly scans the entire image data for any possible abnormality locations, while the subsequent stages of the CAD system refine the candidates list to determine the most probable or significant of these candidates. The candidate generator creates a list of the locations and provides a size estimate for each candidate. A multiscale scale-normalized Laplacian of Gaussian (LoG) filtering method for detecting pulmonary nodules in whole-lung CT scans, presented in this paper, achieves a high sensitivity for both…
Arkansas State University（阿肯色州立大学）；University of Minnesota（明尼苏达大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai University of Finance and Economics（上海财经大学）；Washington University（华盛顿大学）；University of Arkansas for Medical Sciences（阿肯色大学医学科学校区）
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienc…
Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional…
University of Copenhagen（哥本哈根大学）；Rigshospitalet, Copenhagen University Hospital（哥本哈根大学医院 Rigshospitalet）；Hong Kong Baptist University（香港浸会大学）
Feature-based self-explanatory methods explain their classification in terms of human-understandable features. In the medical imaging community, this semantic matching of clinical knowledge adds significantly to the trustworthiness of the AI. However, the cost of additional annotation of features remains a pressing issue. We address this problem by proposing cRedAnno, a data-/annotation-efficient self-explanatory approach for lung nodule diagnosis. cRedAnno considerably reduces the annotation need by introducing self-supervised contrastive learning to alleviate the burden of learning most para…
Median Technologies（Median科技公司）；Univ. Artois（阿图瓦大学）
Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ's surface is developed, and coupled with an automatic extraction of morphological features given by the distribution of mean curvature and mesh energy. An XGBoost supervised classifier is then trained and tested on the 3D-morpho…
University of California, Irvine（加州大学欧文分校）；Lenovo AI Lab（联想人工智能实验室）
Recently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) co…
University of Groningen, University Medical Center Groningen（格罗宁根大学医学中心）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；University of Twente（特温特大学）
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural netwo…
LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering
mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology（孟加拉国工程与技术大学生物医学工程系mHealth实验室）
VUNO Inc.（VUNO公司）；Imperial College London（帝国理工学院）；Asan Medical Center（峨山医疗中心）；University of Ulsan（蔚山大学）
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks for implementation. To address this, we propose a semi-supervised learning method where pseudo-negative labels from unlabeled data are used to further refine the performance of a pulmonary nodule detection network in chest radiographs. After training with the proposed network, the…
University of Patras（帕特雷大学）；School of Medicine（医学院）；Laboratory of Nuclear Medicine（核医学实验室）
Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neura…
Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
Yau Mathematical Sciences Center Tsinghua University（姚数学科学中心清华大学）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院）；China State Key Laboratory of Respiratory Disease（中国呼吸疾病国家重点实验室）；Academy of Mathematics and Systems Science Chinese Academy of Sciences（数学科学学院中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Thoracic Surgery and Oncology The First Affiliated Hospital of Guangzhou Medical University（胸外科与肿瘤科广州医科大学第一附属医院）
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；Sejong University（世宗大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Charles Sturt University（查尔斯特大学）
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both con…
Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance when using two different modes: automated versus assisted (additional remote radiologist review). Methods: The CXR public database (n = 247) of the Japanese Society of Radiological Technology with various types and sizes of lung nodules was analyzed. Eight radiologists evaluated the CXR images with regard to the presence of lung nodules and nodule conspicuity. After radiologist review, the AI so…
Concordia Institute for Information Systems Engineering, Concordia University（康考迪亚大学信息系统工程研究所）；Sunnybrook Health Sciences Centre, University of Toronto（多伦多大学桑尼布鲁克健康科学中心）；McGill University Health Center- Research Institute（麦吉尔大学健康中心研究所）；Babak Imaging Center（巴布克影像中心）；Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）
Lung diseases including infections such as Pneumonia, Tuberculosis, and novel Coronavirus (COVID-19), together with Lung Cancer are significantly widespread and are, typically, considered life threatening. In particular, lung cancer is among the most common and deadliest cancers with a low 5-year survival rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save countless lives. In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated b…
Radboud University Medical Center（拉德堡德大学医学中心）；Polytechnic University of Turin（都灵理工大学）；Istituto Nazionale di Fisica Nucleare（国家核物理研究院）；Radboud University Nijmegen（拉德堡德大学）；Radboud University（拉德堡德大学）；The Chinese University of Hong Kong（香港中文大学）；University of Pisa（比萨大学）
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in o…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Intel Technology India Pvt. Ltd.（英特尔技术印度私人有限公司）
Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT) scan is crucial for its effective clinical management. Nodules which are symptomatic of malignancy occupy about 0.0125 - 0.025\% of volume in a CT scan of a patient. Manual screening of all slices is a tedious task and presents a high risk of human errors. To tackle this problem we propose a computationally efficient two stage framework. In the first stage, a convolutional neural network (CNN) trained adversarially …
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究所（INESC TEC））；Faculty of Engineering of the University of Porto (FEUP)（波尔图大学工程学院（FEUP））；University of Trás-os-Montes e Alto Douro (UTAD)（山后-上杜罗大学（UTAD））；Centro Hospitalar e Universitário de S. João（圣若昂大学医院中心）；Faculty of Medicine of the University of Porto (FMUP)（波尔图大学医学院（FMUP））
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly, time-consuming and prone to variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to clinical routine is challenging. In this study, a new database for the development and testing of pulmonary nodule computer-aided strategies is presented which intends…
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiti…
Harbin Engineering University（哈尔滨工程大学）；Monash University（莫纳什大学）；Beijing Airdoc Technology Co., Ltd.（北京鹰瞳科技发展股份有限公司）；Chongqing Hospital of Traditional Chinese Medicine（重庆市中医院）；Chongqing Renji Hospital of Chinese Academy of Sciences（中国科学院重庆仁济医院）
Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic information. Therefore, the simple binarization of lesions by traditional binary segmentation can result in the loss of diagnostic information. In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i.e., a soft mask…
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumoni…
DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial
Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院放射科，疑难重症及罕见病国家重点实验室）；Theranostics and Translational Research Center, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院临床医学研究所，国家转化医学基础设施，诊疗与转化研究中心，疑难重症及罕见病国家重点实验室）；Artificial Intelligence Lab, Deepwise Healthcare（深睿医疗人工智能实验室）；Department of Epidemiology and Health Statistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院基础医学研究所流行病学与卫生统计学系）；Department of Biostatistics, Peking University First Hospital（北京大学第一医院生物统计室）；Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University（湖北理工学院附属医院黄石市中心医院放射科）；Department of Radiology, Wuhan Third Hospital, Tongren Hospital of Wuhan University（武汉大学同仁医院武汉市第三医院放射科）；+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院4+4医学博士项目）；Department of Medicine Imaging, School of Clinical Medicine, Southwest Medical University（西南医科大学临床医学院医学影像系）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心 Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung …
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach
Renmin University of China（中国人民大学）；Capital Medical University（首都医科大学）；Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing Institute of Respiratory Medicine（北京呼吸疾病研究所）
King’s College London（伦敦国王学院）；Guy’s & St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Imperial College Healthcare NHS Trust（帝国理工学院医疗国民保健服务信托）
Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak label…
Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations…
Dali University（大理大学）；Liuzhou Vocational & Technical College（柳州职业技术学院）；The Second Xiangya Hospital of Central South University（中南大学湘雅二医院）；Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer（湖南省肺癌早期诊断与精准治疗重点实验室）；Early-Stage Lung Cancer Center（早期肺癌中心）
Detection of pulmonary nodules by CT is used for screening lung cancer in early stages.omputer aided diagnosis (CAD) based on deep-learning method can identify the suspected areas of pulmonary nodules in CT images, thus improving the accuracy and efficiency of CT diagnosis. The accuracy and robustness of deep learning models. Method:In this paper, we explore (1) the data augmentation method based on the generation model and (2) the model structure improvement method based on the embedding mechanism. Two strategies have been introduced in this study: a new data augmentation method and a embeddi…
Jordan University of Science and Technology（约旦科技大学）
Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detec…
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. Nevertheless, with physical models able to preserve morphological details but at the cost of extremely long processing time, existing DL methods face challenges of gathering sufficient/qualitative ground truth (GT) for robust training, thus leading to failure in maintaining clinically acceptable false positive rates. We hereby propose a generalizable yet efficient workfl…
Eurecat, Centre Tecnòlogic de Catalunya（加泰罗尼亚技术中心Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高级研究院）
Nodule malignancy assessment is a complex, time-consuming and error-prone task. Current clinical practice requires measuring changes in size and density of the nodule at different time-points. State of the art solutions rely on 3D convolutional neural networks built on pulmonary nodules obtained from single CT scan per patient. In this work, we propose a two-stream 3D convolutional neural network that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points. Best results achieve 77% of F1-score in test with an increment of 9% an…
Though large-scale datasets are essential for training deep learning systems, it is expensive to scale up the collection of medical imaging datasets. Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance. In this paper, we propose the class-aware adversarial synthesis framework to synthesize lung nodules in CT images. The framework is built with a coarse-to-fine patch in-painter (generator) and tw…
Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey
College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610225, China（计算机科学与人工智能学院，西南民族大学）；The Australian National University, Canberra ACT 2601, Australia（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China（计算机科学与技术学校，北京交通大学）；Hunan university of technology, Zhuzhou, 410072, China（湖南理工大学）；University of Adelaide, Adelaide, South Australia 5005, Australia（阿德莱德大学）；La Trobe University, Melbourne, Victoria 3086, Australia（拉筹伯大学）
Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）；Georgia Institute of Technology（佐治亚理工学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Cl…
A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK（沃里克大学计算机科学系组织图像分析中心，英国）；Royal Stoke University Hospital, University Hospitals of North Midlands, UK（北中部大学医院皇家斯托医院，英国）
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（国家空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）
Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come from the ambiguous labels provided by annotators, since deep learning models may learn, even amplify, the bias embedded in them. In this paper, we propose a multi-view "divide-and-rule" (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction. According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable s…
A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging
School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；College of Surveying and GeoInformatics, Tongji University（同济大学测绘与地理信息学院）；School of Computer Science, Shaanxi Normal University（陕西师范大学计算机学院）
Wuhan University（武汉大学）；Hubei Luojia Laboratory（湖北珞珈实验室）；The University of Electro-Communications（电气通信大学）；JD Explore Academy（京东探索研究院）；Dianei Technology（点内科技）；Shanghai Jiao Tong University（上海交通大学）；EPFL（洛桑联邦理工学院）
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To a…
Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis, greatly easing the workload while increasing diagnosis accuracy. Recent development of deep learning, greatly improved the performance of CAD systems. However, lack of model reliability and interpret…
The City College of New York（纽约城市学院）；UMass CICS（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Google AI（谷歌人工智能）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the res…
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Wuhan University（武汉大学）；Artificial Intelligence Institute of Wuhan University（武汉大学人工智能研究院）；Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Dianei Technology（点内科技）
Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a ne…
Yandex School of Data Analysis（Yandex数据分析学院）；Tel Aviv University（特拉维夫大学）
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR). We discuss the theoretical limitations of the available datasets and overcome …
Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules
University of California, Davis（加州大学戴维斯分校）；University of Washington（华盛顿大学）；Northern Arizona University（北亚利桑那大学）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣迭戈分校）
Dalian University of Technology（大连理工大学）；Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province（辽宁省泛在网络与服务软件重点实验室）
More and more attention has been paid to the segmentation of pulmonary nodules. Among the current methods based on deep learning, 3D segmentation methods directly input 3D images, which takes up a lot of memory and brings huge computation. However, most of the 2D segmentation methods with less parameters and calculation have the problem of lacking spatial relations between slices, resulting in poor segmentation performance. In order to solve these problems, we propose an adjacent slice feature guided 2.5D network. In this paper, we design an adjacent slice feature fusion model to introduce inf…
Eurecat Centre Tecnològic de Catalunya（加泰罗尼亚埃urecat技术中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；ICREA（加泰罗尼亚研究与高等研究院）
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated …
The City College of New York（纽约城市学院）；UMass CICS（马萨诸塞大学信息与计算机科学学院）；Google AI（谷歌人工智能）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limits the automatic diagnosis in routine clinical practice. Moreover, the CT scans collected from multiple manufacturers may affect the robustness of Computer-aided diagnosis (CAD) due to the differences in intensity scales and machine noises. In this paper, we propose a novel …
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in orde…
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
Hupan Lab（湖畔实验室）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
